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<h1>Base predictors for specific usages.</h1>
<p>
If you are a user, please go directly to subpackages containing concrete
implementations to predict specific elements.
</p>
<p>
If you're developer, you should look for a predictor that performs a similar
task in this package and extend it fixing initialization parameters.
<p/>
<p>
The main ideas of classes in this package are:
<ul>
  <li>Classes <code>OnePredictorPerTypeOfDayOfWeek</code>,
          <code>OnePredictorPerBin</code> and
          <code>OnePredictorPerBinAndTypeOfDayOfWeek</code> are wrappers
          of several predictors of the same type given at construction time.
   </li>
   <li>Classes <code>PCACombiningSeveralDays</code> and 
          <code>PCADayByDayPredictor</code> act as base classes for other
          Principal Component Analysis: the first one takes data creating
          a matrix with one day per column (recording information for 
          several days and one one product) and the second one takes data
          to create a matrix with one product daily data per column
          (recording information for several products in only one day).
    </li>
    <li><code>StaticDayByDayPredictor</code> is the base class for predictors
         like <code>PCADayByDayPredictor</code> that after recording all
         information for a day, generate a prediction for the next day
         as a whole.</li>
    <li>In the opposite, <code>DynamicIntradePredictorListener</code> is the
         base class of predictors that generate predictions while they
         are learning.</li>
    <li><code>QuasiDynamicIntradayPredictorListener</code> uses some data
         learnt at the begining of the day to forecast the whole day.</li>
</ul>
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